24 citations · 68 across the 19 of their papers we have counts for
19 papers
Discrete-Space Generative AI Pipeline for Semantic Transmission of Signals
Silvija Kokalj-Filipovic, Yagna Kaasaragadda
We introduce Discernment, a semantic communication system that transmits the meaning of physical signals (baseband radio and audio) over a technical channel using GenAI models oper…
A look at adversarial attacks on radio waveforms from discrete latent space
Attanasia Garuso, Silvija Kokalj-Filipovic, Yagna Kaasaragadda
Having designed a VQVAE that maps digital radio waveforms into discrete latent space, and yields a perfectly classifiable reconstruction of the original data, we here analyze the a…
ReFormer: Generating Radio Fakes for Data Augmentation
Yagna Kaasaragadda, Silvija Kokalj-Filipovic
We present ReFormer, a generative AI (GAI) model that can efficiently generate synthetic radio-frequency (RF) data, or RF fakes, statistically similar to the data it was trained on…
VQalAttent: a Transparent Speech Generation Pipeline based on Transformer-learned VQ-VAE Latent Space
Armani Rodriguez, Silvija Kokalj-Filipovic
Generating high-quality speech efficiently remains a key challenge for generative models in speech synthesis. This paper introduces VQalAttent, a lightweight model designed to gene…
Deep-Learned Compression for Radio-Frequency Signal Classification
Armani Rodriguez, Yagna Kaasaragadda, Silvija Kokalj-Filipovic
Next-generation cellular concepts rely on the processing of large quantities of radio-frequency (RF) samples. This includes Radio Access Networks (RAN) connecting the cellular fron…
Large Scale Radio Frequency Signal Classification
Luke Boegner, Manbir Gulati, Garrett Vanhoy +5
Existing datasets used to train deep learning models for narrowband radio frequency (RF) signal classification lack enough diversity in signal types and channel impairments to suff…